OpenRaiser/Intern-Atlas huggingface dataset:
Network Coding with Link Layer Cooperation in Wireless Mesh Networks
Venue: arXiv cs.NI 2018
Year: 2018

In recent years, network coding has emerged as an innovative method that helps wireless network approaches its maximum capacity, by combining multiple unicasts in one broadcast. However, the majority of research conducted in this area is yet to fully utilize the broadcasting nature of wireless networks, and still assumes fixed route between the source and destination that every packet should travel through. This assumption not only limits coding opportunities, but can also cause buffer overflow in some specific intermediate nodes. Although some studies considered scattering of the flows dynamically in the network, they still face some limitations. This paper explains pros and cons of some prominent research in network coding and proposes FlexONC (Flexible and Opportunistic Network Coding) as a solution to such issues. The performance results show that FlexONC outperforms previous methods especially in worse quality networks, by better utilizing redundant packets spread in the network.
Push recovery with stepping strategy based on time-projection control
Venue: arXiv cs.RO 2018
Year: 2018

In this paper, we present a simple control framework for on-line push recovery with dynamic stepping properties. Due to relatively heavy legs in our robot, we need to take swing dynamics into account and thus use a linear model called 3LP which is composed of three pendulums to simulate swing and torso dynamics. Based on 3LP equations, we formulate discrete LQR controllers and use a particular time-projection method to adjust the next footstep location on-line during the motion continuously. This adjustment, which is found based on both pelvis and swing foot tracking errors, naturally takes the swing dynamics into account. Suggested adjustments are added to the Cartesian 3LP gaits and converted to joint-space trajectories through inverse kinematics. Fixed and adaptive foot lift strategies also ensure enough ground clearance in perturbed walking conditions. The proposed structure is robust, yet uses very simple state estimation and basic position tracking. We rely on the physical series elastic actuators to absorb impacts while introducing simple laws to compensate their tracking bias. Extensive experiments demonstrate the functionality of different control blocks and prove the effectiveness of time-projection in extreme push recovery scenarios. We also show self-produced and emergent walking gaits when the robot is subject to continuous dragging forces. These gaits feature dynamic walking robustness due to relatively soft springs in the ankles and avoiding any Zero Moment Point (ZMP) control in our proposed architecture.
Blind Demixing for Low-Latency Communication
Venue: arXiv cs.IT 2018
Year: 2018

In the next generation wireless networks, lowlatency communication is critical to support emerging diversified applications, e.g., Tactile Internet and Virtual Reality. In this paper, a novel blind demixing approach is developed to reduce the channel signaling overhead, thereby supporting low-latency communication. Specifically, we develop a low-rank approach to recover the original information only based on a single observed vector without any channel estimation. Unfortunately, this problem turns out to be a highly intractable non-convex optimization problem due to the multiple non-convex rankone constraints. To address the unique challenges, the quotient manifold geometry of product of complex asymmetric rankone matrices is exploited by equivalently reformulating original complex asymmetric matrices to the Hermitian positive semidefinite matrices. We further generalize the geometric concepts of the complex product manifolds via element-wise extension of the geometric concepts of the individual manifolds. A scalable Riemannian trust-region algorithm is then developed to solve the blind demixing problem efficiently with fast convergence rates and low iteration cost. Numerical results will demonstrate the algorithmic advantages and admirable performance of the proposed algorithm compared with the state-of-art methods.
Approximate FPGA-based LSTMs under Computation Time Constraints
Venue: arXiv cs.CV 2018
Year: 2018

Recurrent Neural Networks and in particular Long Short-Term Memory (LSTM) networks have demonstrated state-of-the-art accuracy in several emerging Artificial Intelligence tasks. However, the models are becoming increasingly demanding in terms of computational and memory load. Emerging latency-sensitive applications including mobile robots and autonomous vehicles often operate under stringent computation time constraints. In this paper, we address the challenge of deploying computationally demanding LSTMs at a constrained time budget by introducing an approximate computing scheme that combines iterative low-rank compression and pruning, along with a novel FPGA-based LSTM architecture. Combined in an end-to-end framework, the approximation method's parameters are optimised and the architecture is configured to address the problem of high-performance LSTM execution in time-constrained applications. Quantitative evaluation on a real-life image captioning application indicates that the proposed methods required up to 6.5x less time to achieve the same application-level accuracy compared to a baseline method, while achieving an average of 25x higher accuracy under the same computation time constraints.
Multi-platform Version of StarCraft: Brood War in a Docker Container: Technical Report
Venue: arXiv cs.AI 2018
Year: 2018

We present a dockerized version of a real-time strategy game StarCraft: Brood War, commonly used as a domain for AI research, with a pre-installed collection of AI developement tools supporting all the major types of StarCraft bots. This provides a convenient way to deploy StarCraft AIs on numerous hosts at once and across multiple platforms despite limited OS support of StarCraft. In this technical report, we describe the design of our Docker images and present a few use cases.
Topic-Based Influence Computation in Social Networks under Resource Constraints
Venue: arXiv cs.SI 2018
Year: 2018

As social networks are constantly changing and evolving, methods to analyze dynamic social networks are becoming more important in understanding social trends. However, due to the restrictions imposed by the social network service providers, the resources available to fetch the entire contents of a social network are typically very limited. As a result, analysis of dynamic social network data requires maintaining an approximate copy of the social network for each time period, locally. In this paper, we study the problem of dynamic network and text fetching with limited probing capacities, for identifying and maintaining influential users as the social network evolves. We propose an algorithm to probe the relationships (required for global influence computation) as well as posts (required for topic-based influence computation) of a limited number of users during each probing period, based on the influence trends and activities of the users. We infer the current network based on the newly probed user data and the last known version of the network maintained locally. Additionally, we propose to use link prediction methods to further increase the accuracy of our network inference. We employ PageRank as the metric for influence computation. We illustrate how the proposed solution maintains accurate PageRank scores for computing global influence, and topic-sensitive weighted PageRank scores for topic-based influence. The latter relies on a topic-based network constructed via weights determined by semantic analysis of posts and their sharing statistics. We evaluate the effectiveness of our algorithms by comparing them with the true influence scores of the full and up-to-date version of the network, using data from the micro-blogging service Twitter. Results show that our techniques significantly outperform baseline methods and are superior to state-of-the-art techniques from the literature.
Cross-modal Embeddings for Video and Audio Retrieval
Venue: arXiv cs.IR 2018
Year: 2018

The increasing amount of online videos brings several opportunities for training self-supervised neural networks. The creation of large scale datasets of videos such as the YouTube-8M allows us to deal with this large amount of data in manageable way. In this work, we find new ways of exploiting this dataset by taking advantage of the multi-modal information it provides. By means of a neural network, we are able to create links between audio and visual documents, by projecting them into a common region of the feature space, obtaining joint audio-visual embeddings. These links are used to retrieve audio samples that fit well to a given silent video, and also to retrieve images that match a given a query audio. The results in terms of Recall@K obtained over a subset of YouTube-8M videos show the potential of this unsupervised approach for cross-modal feature learning. We train embeddings for both scales and assess their quality in a retrieval problem, formulated as using the feature extracted from one modality to retrieve the most similar videos based on the features computed in the other modality.
Trading the Twitter Sentiment with Reinforcement Learning
Venue: arXiv cs.AI 2018
Year: 2018

This paper is to explore the possibility to use alternative data and artificial intelligence techniques to trade stocks. The efficacy of the daily Twitter sentiment on predicting the stock return is examined using machine learning methods. Reinforcement learning(Q-learning) is applied to generate the optimal trading policy based on the sentiment signal. The predicting power of the sentiment signal is more significant if the stock price is driven by the expectation of the company growth and when the company has a major event that draws the public attention. The optimal trading strategy based on reinforcement learning outperforms the trading strategy based on the machine learning prediction.
Sample-Efficient Reinforcement Learning through Transfer and Architectural Priors
Venue: arXiv cs.LG 2018
Year: 2018

Recent work in deep reinforcement learning has allowed algorithms to learn complex tasks such as Atari 2600 games just from the reward provided by the game, but these algorithms presently require millions of training steps in order to learn, making them approximately five orders of magnitude slower than humans. One reason for this is that humans build robust shared representations that are applicable to collections of problems, making it much easier to assimilate new variants. This paper first introduces the idea of automatically-generated game sets to aid in transfer learning research, and then demonstrates the utility of shared representations by showing that models can substantially benefit from the incorporation of relevant architectural priors. This technique affords a remarkable 50x positive transfer on a toy problem-set.
Identity-preserving Face Recovery from Portraits
Venue: arXiv cs.CV 2018
Year: 2018

Recovering the latent photorealistic faces from their artistic portraits aids human perception and facial analysis. However, a recovery process that can preserve identity is challenging because the fine details of real faces can be distorted or lost in stylized images. In this paper, we present a new Identity-preserving Face Recovery from Portraits (IFRP) to recover latent photorealistic faces from unaligned stylized portraits. Our IFRP method consists of two components: Style Removal Network (SRN) and Discriminative Network (DN). The SRN is designed to transfer feature maps of stylized images to the feature maps of the corresponding photorealistic faces. By embedding spatial transformer networks into the SRN, our method can compensate for misalignments of stylized faces automatically and output aligned realistic face images. The role of the DN is to enforce recovered faces to be similar to authentic faces. To ensure the identity preservation, we promote the recovered and ground-truth faces to share similar visual features via a distance measure which compares features of recovered and ground-truth faces extracted from a pre-trained VGG network. We evaluate our method on a large-scale synthesized dataset of real and stylized face pairs and attain state of the art results. In addition, our method can recover photorealistic faces from previously unseen stylized portraits, original paintings and human-drawn sketches.
A Game-Theoretic Approach for NOMA-ALOHA
Venue: arXiv cs.IT 2018
Year: 2018

Non-orthogonal multiple access (NOMA) can improve the spectral efficiency by exploiting the power domain and successive interference cancellation (SIC), and it can be applied to various transmission schemes including random access that plays a crucial role in the Internet of Things (IoT) to support connectivity for a number of devices with sparse activity. In this paper, we formulate a game when NOMA is applied to ALOHA to decide the transmission probability. We consider a payoff function based on an energy-efficiency metric and drive the mixed strategy Nash equilibrium (NE).
Applicable Mathematics in a Minimal Computational Theory of Sets
Venue: arXiv cs.LO 2018
Year: 2018

In previous papers on this project a general static logical framework for formalizing and mechanizing set theories of different strength was suggested, and the power of some predicatively acceptable theories in that framework was explored. In this work we first improve that framework by enriching it with means for coherently extending by definitions its theories, without destroying its static nature or violating any of the principles on which it is based. Then we turn to investigate within the enriched framework the power of the minimal (predicatively acceptable) theory in it that proves the existence of infinite sets. We show that that theory is a computational theory, in the sense that every element of its minimal transitive model is denoted by some of its closed terms. (That model happens to be the second universe in Jensen's hierarchy.) Then we show that already this minimal theory suffices for developing very large portions (if not all) of scientifically applicable mathematics. This requires treating the collection of real numbers as a proper class, that is: a unary predicate which can be introduced in the theory by the static extension method described in the first part of the paper.
Bridging the Gap: Simultaneous Fine Tuning for Data Re-Balancing
Venue: arXiv cs.CV 2018
Year: 2018

There are many real-world classification problems wherein the issue of data imbalance (the case when a data set contains substantially more samples for one/many classes than the rest) is unavoidable. While under-sampling the problematic classes is a common solution, this is not a compelling option when the large data class is itself diverse and/or the limited data class is especially small. We suggest a strategy based on recent work concerning limited data problems which utilizes a supplemental set of images with similar properties to the limited data class to aid in the training of a neural network. We show results for our model against other typical methods on a real-world synthetic aperture sonar data set. Code can be found at github.com/JohnMcKay/dataImbalance.
Tamarin: Concolic Disequivalence for MIPS
Venue: arXiv cs.PL 2018
Year: 2018

Given two MIPS programs, when are they equivalent? At first glance, this is tricky to define, because of the unstructured nature of assembly code. We propose the use of alternating concolic execution to detect whether two programs are disequivalent. We have implemented our approach in a tool called Tamarin, which includes a MIPS emulator instrumented to record symbolic traces, as well as a concolic execution engine that integrates with the Z3 solver. We show that Tamarin is able to reason about program disequivalence in a number of scenarios, without any a-priori knowledge about the MIPS programs under consideration.
Codes induced by alternative codes
Venue: arXiv cs.FL 2018
Year: 2018

Alternative codes, an extension of the notion of ordinary codes, have been first introduced and considered by P. T. Huy et al. in 2004. As seen below, every alternative code, in its turn, defines an ordinary code. Such codes are called codes induced by alternative codes or alt-induced codes, for short. In this paper we consider these alt-induced codes and subclasses of them. In particular, characteristic properties of such codes are established, and an algorithm to check whether a finite code is alt-induced or not is proposed.
An Energy Conserving Routing Scheme for Wireless Body Sensor Nanonetwork Communication
Venue: arXiv cs.NI 2018
Year: 2018

Current developments in nanotechnology make electromagnetic communication (EC) possible at the nanoscale for applications involving Wireless [Body] Sensor Networks (W[B]SNs). This specialized branch of WSN has emerged as an important research area contributing to medical treatment, social welfare, and sports. The concept is based on the interaction of integrated nanoscale machines by means of wireless communications. One key hurdle for advancing nanocommunications is the lack of an apposite networking protocol to address the upcoming needs of the nanonetworks. Recently, some key challenges have been identified, such as nanonodes with extreme energy constraints, limited computational capabilities, Terahertz frequency bands with limited transmission range, etc., in designing protocols for wireless nanosensor networks (WNN). This work proposes an improved performance scheme of nanocommunication over Terahertz bands for wireless BSNs making it suitable for smart e-health applications. The scheme contains -- a new energy-efficient forwarding routine for EC in WNN consisting of hybrid clusters with centralized scheduling, a model designed for channel behavior taking into account the aggregated impact of molecular absorption, spreading loss, and shadowing, and an energy model for energy harvesting and consumption. The outage probability is derived for both single and multilinks and extended to determine the outage capacity. The outage probability for a multilink is derived using a cooperative fusion technique at a predefined fusion node. Simulated using a Nano-Sim simulator, performance of the proposed model has been evaluated for energy efficiency, outage capacity, and outage probability. The results demonstrate the efficiency of the proposed scheme through maximized energy utilization in both single and multihop communication, multisensor fusion enhances the link quality of the transmission.
Graph Memory Networks for Molecular Activity Prediction
Venue: arXiv cs.LG 2018
Year: 2018

Molecular activity prediction is critical in drug design. Machine learning techniques such as kernel methods and random forests have been successful for this task. These models require fixed-size feature vectors as input while the molecules are variable in size and structure. As a result, fixed-size fingerprint representation is poor in handling substructures for large molecules. In addition, molecular activity tests, or a so-called BioAssays, are relatively small in the number of tested molecules due to its complexity. Here we approach the problem through deep neural networks as they are flexible in modeling structured data such as grids, sequences and graphs. We train multiple BioAssays using a multi-task learning framework, which combines information from multiple sources to improve the performance of prediction, especially on small datasets. We propose Graph Memory Network (GraphMem), a memory-augmented neural network to model the graph structure in molecules. GraphMem consists of a recurrent controller coupled with an external memory whose cells dynamically interact and change through a multi-hop reasoning process. Applied to the molecules, the dynamic interactions enable an iterative refinement of the representation of molecular graphs with multiple bond types. GraphMem is capable of jointly training on multiple datasets by using a specific-task query fed to the controller as an input. We demonstrate the effectiveness of the proposed model for separately and jointly training on more than 100K measurements, spanning across 9 BioAssay activity tests.
Towards General Distributed Resource Selection
Venue: arXiv cs.DC 2018
Year: 2018

The advantages of distributing workloads and utilizing multiple distributed resources are now well established. The type and degree of heterogeneity of distributed resources is increasing, and thus determining how to distribute the workloads becomes increasingly difficult, in particular with respect to the selection of suitable resources. We formulate and investigate the resource selection problem in a way that it is agnostic of specific task and resource properties, and which is generalizable to range of metrics. Specifically, we developed a model to describe the requirements of tasks and to estimate the cost of running that task on an arbitrary resource using baseline measurements from a reference machine. We integrated our cost model with the Condor matchmaking algorithm to enable resource selection. Experimental validation of our model shows that it provides execution time estimates with 157-171% error on XSEDE resources and 18-31% on OSG resources. We use the task execution cost model to select resources for a bag-of-tasks of up to 1024 GROMACS MD simulations across the target resources. Experiments show that using the model's estimates reduces the workload's time-to-completion up to ~85% when compared to the random distribution of workload across the same resources.
Stable Marriage with Multi-Modal Preferences
Venue: arXiv cs.MA 2018
Year: 2018

We introduce a generalized version of the famous Stable Marriage problem, now based on multi-modal preference lists. The central twist herein is to allow each agent to rank its potentially matching counterparts based on more than one "evaluation mode" (e.g., more than one criterion); thus, each agent is equipped with multiple preference lists, each ranking the counterparts in a possibly different way. We introduce and study three natural concepts of stability, investigate their mutual relations and focus on computational complexity aspects with respect to computing stable matchings in these new scenarios. Mostly encountering computational hardness (NP-hardness), we can also spot few islands of tractability and make a surprising connection to the \textsc{Graph Isomorphism} problem.
Data Augmentation for Brain-Computer Interfaces: Analysis on Event-Related Potentials Data
Venue: arXiv cs.CV 2018
Year: 2018

On image data, data augmentation is becoming less relevant due to the large amount of available training data and regularization techniques. Common approaches are moving windows (cropping), scaling, affine distortions, random noise, and elastic deformations. For electroencephalographic data, the lack of sufficient training data is still a major issue. We suggest and evaluate different approaches to generate augmented data using temporal and spatial/rotational distortions. Our results on the perception of rare stimuli (P300 data) and movement prediction (MRCP data) show that these approaches are feasible and can significantly increase the performance of signal processing chains for brain-computer interfaces by 1% to 6%.